Application of the BP Neural Network Model of Gray Relational Analysis in Economic Management

Abstract

According to the BP neural network algorithm, for the problem that the input variables of the BP network cannot be automatically optimized during the modeling process of the multivariable complex system, it is used to establish a neural network optimization algorithm according to the gray correlation analysis (GM2 BPANN). Using the data of China’s grain production forecast, the stepwise regression method and the gray GM (1, N) model method were compared and tested. The results show that the new model can comprehensively and extensively process a large number of input variables by using the concept of the gray correlation degree, without having to go through special subjective screening and hence improving the adaptability of the BP network, and at the same time, it can obtain better prediction accuracy and stability. Through the empirical test, the prediction ability of the three methods of regression, GM (1, N) gray system, and GM-BPANN model is compared. It is proved that the GM-BPANN optimization algorithm that combines the gray relational analysis and BP neural network method can enhance the multivariable processing ability and network adaptability of the BP network and has good prediction accuracy and stability.

[Retracted] A Decision Model for Public Economic Environment Monitoring and Management Decision Using Improved Ant Colony Algorithm

“…Therefore, it is necessary to innovate and reshape it with the management doctrine with economy and efficiency as the core values and rebuild the process and mode of public management. The specific process of public economic management is the effective combination of resources and human resources for the economic activities within the functional scope of the public sector through planning, decision-making, organization, leadership, coordination, evaluation, and other links [ 10 ]. The innovation of this paper lies in the following: The system management method based on overall coordination is proposed to guide the implementation of public economic management decisions.…”

Integration of Domain Knowledge and Data-Driven Modeling Evaluation Process for Predicting Minimum Miscible Pressure of CO2–Oil Systems in CCUS

“…Gray relational analysis fundamental tenet is to compare various series to a predetermined reference series and determine how closely related they are by their geometric relationships . A straightforward and trustworthy technique for analyzing data is gray relational analysis . According to Sedgwick, the Pearson correlation coefficient is mostly utilized to analyze the linear relationships between data, whereas Kendall and Spearman correlation coefficients are employed to determine the correlation between data by resolving the rank of the data matrix.…”

“…28 A straightforward and trustworthy technique for analyzing data is gray relational analysis. 29 According to Sedgwick, 30 the Pearson correlation coefficient is mostly utilized to analyze the linear relationships between data, whereas Kendall and Spearman 31 correlation coefficients are employed to determine the correlation between data by resolving the rank of the data matrix. The positive and negative Pearson, Kendall, and Spearman correlation coefficients represent positive and negative correlation, respectively, and the size represents the degree of correlation.…”

Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network

“…GRA provides a better way to solve this problem [41][42][43][44][45]. It analyzes the geometric similarity from the dependent variable sequence to the independent one to determine the connection between different independent variable sequences and dependent variables.…”